Springer, 2015. — 524 p.Nowadays, we are witnessing an exponential growth in data sets, coined as Big Data era. The data being generated in large Internet-based IT systems is becoming a cornerstone for cyber-physical systems, administration, enterprises, businesses, academia and all human activity fields. Indeed, there is data being generated everywhere: in IT systems, biology, genomics, financial, geospatial, social networks, transportation, logistics, telecommunications, engineering, digital content, to name a few. Unlike recent past where the focus of IT systems was on functional requirements and services, now data is seen as a new asset and data technologies are needed to support IT systems with knowledge, analytics and decision support systems.Researchers and developers are facing challenges in dealing with this data deluge. Challenges arise due to the extremely large volumes of data, their heterogenous nature (structured & unstructured) and the pace at which data is generated requiring both offline and online processing of large streams of data as well as storing, security, anonymicity, etc. Obviously, most traditional database solutions may not be able to cope with such challenges and non-traditional database and storage solutions are imperative today. Novel modelling, algorithms, software solutions and methodologies to cover full data cycle (from data gathering to visualisation and interaction) are in need for investigation.This Springer book brings together nineteen chapter contributions on new models and analytic approaches for the modelling of large data sets, efficient data processing (online/offline) and analysis (analytics, mining, etc.) to enable next generation data aware systems offering quality content and innovative services in a reliable and scalable way. The book chapters critically analyze the state of the art and envision the road ahead on modelling, analysis and optimisation models for next generatation big data technologies. Finally, benchmarking, frameworks, applications, case studies and best practices for big data are also included in the book.